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Topics: Radio Program, Machine learning, Neural networks, Cybernetics, Member states of the Commonwealth of...

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01:00:00AM-06:00:00AM GMT — As BBC Radio 5 live 23/02/2020 BBC Radio Norfolk joins BBC Radio 5 live.

Topics: Radio Program, Cybernetics, Artificial intelligence, Machine learning, Learning, Clinical research,...

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Mar 6, 2021
03/21

by
Merge Conflict

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At Microsoft Build 2018 we sat down with the legendary Jb Evain. The man behind libraries and technologies we use every day including Mono.Cecil and the Mono linker itself. We discuss all this and his passion for creating the Visual Studio tools for Unity and everything new that Unity has to offer.Follow UsFrank: Twitter, Blog, GitHubJames: Twitter, Blog, GitHubMerge Conflict: Twitter, Facebook, WebsiteMusic : Amethyst Seer - Citrine by Adventureface⭐⭐ Review Us ⭐⭐SUPPORT US ON PATREON:...

Topics: Podcast, Xamarin, iOS, Android, UWP, Microsoft, .NET, C#, F#, Machine Learning, AI, Apps, Apple,...

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Topics: Radio Program, Machine learning, Neural networks, Member states of the Commonwealth of Nations,...

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Topics: Radio Program, Machine learning, Neural networks, Member states of the Commonwealth of Nations,...

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01:00:00AM-06:00:00AM GMT — As BBC Radio 5 live 23/02/2020 BBC Radio Stoke joins BBC Radio 5 live.

Topics: Radio Program, Cybernetics, Artificial intelligence, Machine learning, Learning, Clinical research,...

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Topics: Radio Program, Machine learning, Neural networks, Member states of the Commonwealth of Nations,...

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01:00:00AM-05:00:00AM BST — As BBC Radio 5 live 26/09/2019 BBC Radio Stoke joins BBC Radio 5 live.

Topics: Radio Program, Artificial intelligence, Learning, Cybernetics, Machine learning, Climate change,...

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5.0

Jun 30, 2018
06/18

by
Yasin Abbasi-Yadkori; Gergely Neu

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We study online learning of finite Markov decision process (MDP) problems when a side information vector is available. The problem is motivated by applications such as clinical trials, recommendation systems, etc. Such applications have an episodic structure, where each episode corresponds to a patient/customer. Our objective is to compete with the optimal dynamic policy that can take side information into account. We propose a computationally efficient algorithm and show that its regret is at...

Topics: Machine Learning, Computing Research Repository, Statistics, Learning

Source: http://arxiv.org/abs/1406.6812

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15/03/2017 GMT True life stories

Topics: BBC, Radio Program, Outlook, Intellectual property law, Artificial intelligence, Chief executive...

Source: http://www.bbc.co.uk/programmes/p04w50v2

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Jun 29, 2018
06/18

by
Swami Sankaranarayanan; Azadeh Alavi; Carlos Castillo; Rama Chellappa

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Despite significant progress made over the past twenty five years, unconstrained face verification remains a challenging problem. This paper proposes an approach that couples a deep CNN-based approach with a low-dimensional discriminative embedding learned using triplet probability constraints to solve the unconstrained face verification problem. Aside from yielding performance improvements, this embedding provides significant advantages in terms of memory and for post-processing operations...

Topics: Machine Learning, Statistics, Computer Vision and Pattern Recognition, Computing Research...

Source: http://arxiv.org/abs/1604.05417

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8.0

Jun 27, 2018
06/18

by
Cengiz Pehlevan; Tao Hu; Dmitri B. Chklovskii

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Neural network models of early sensory processing typically reduce the dimensionality of streaming input data. Such networks learn the principal subspace, in the sense of principal component analysis (PCA), by adjusting synaptic weights according to activity-dependent learning rules. When derived from a principled cost function these rules are nonlocal and hence biologically implausible. At the same time, biologically plausible local rules have been postulated rather than derived from a...

Topics: Machine Learning, Statistics, Quantitative Biology, Neural and Evolutionary Computing, Neurons and...

Source: http://arxiv.org/abs/1503.00669

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Jun 27, 2018
06/18

by
Luke Vilnis; David Belanger; Daniel Sheldon; Andrew McCallum

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Many inference problems in structured prediction are naturally solved by augmenting a tractable dependency structure with complex, non-local auxiliary objectives. This includes the mean field family of variational inference algorithms, soft- or hard-constrained inference using Lagrangian relaxation or linear programming, collective graphical models, and forms of semi-supervised learning such as posterior regularization. We present a method to discriminatively learn broad families of inference...

Topics: Machine Learning, Learning, Computing Research Repository, Statistics, Computation and Language

Source: http://arxiv.org/abs/1503.01397

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4.0

Jun 29, 2018
06/18

by
André L. V. Coelho; Fabrício O. de França

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Perceptrons are neuronal devices capable of fully discriminating linearly separable classes. Although straightforward to implement and train, their applicability is usually hindered by non-trivial requirements imposed by real-world classification problems. Therefore, several approaches, such as kernel perceptrons, have been conceived to counteract such difficulties. In this paper, we investigate an enhanced perceptron model based on the notion of contrastive biclusters. From this perspective, a...

Topics: Machine Learning, Statistics, Neural and Evolutionary Computing, Computing Research Repository,...

Source: http://arxiv.org/abs/1603.06859

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3.0

Jun 29, 2018
06/18

by
Vince Lyzinski; Keith Levin; Donniell E. Fishkind; Carey E. Priebe

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Given a graph in which a few vertices are deemed interesting a priori, the vertex nomination task is to order the remaining vertices into a nomination list such that there is a concentration of interesting vertices at the top of the list. Previous work has yielded several approaches to this problem, with theoretical results in the setting where the graph is drawn from a stochastic block model (SBM), including a vertex nomination analogue of the Bayes optimal classifier. In this paper, we prove...

Topics: Machine Learning, Statistics

Source: http://arxiv.org/abs/1607.01369

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3.0

Jun 29, 2018
06/18

by
Michalis Michaelides; Dimitrios Milios; Jane Hillston; Guido Sanguinetti

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Dynamical systems with large state-spaces are often expensive to thoroughly explore experimentally. Coarse-graining methods aim to define simpler systems which are more amenable to analysis and exploration; most current methods, however, focus on a priori state aggregation based on similarities in transition rates, which is not necessarily reflected in similar behaviours at the level of trajectories. We propose a way to coarsen the state-space of a system which optimally preserves the...

Topics: Machine Learning, Systems and Control, Computing Research Repository, Statistics

Source: http://arxiv.org/abs/1606.01111

Topics: Radio Program, Cybernetics, Artificial intelligence, Learning, Technology in society, Formal...

Topics: Radio Program, Computational linguistics, Machine translation, Cybernetics, Political terminology,...

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Feb 23, 2021
02/21

by
Changelog Master Feed

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At this year's Government & Public Sector R Conference (or R|Gov) our very own Daniel Whitenack moderated a panel on how AI practitioners can engage with governments on AI for good projects. That discussion is being republished in this episode for all our listeners to enjoy! The panelists were Danya Murali from Arcadia Power and Emily Martinez from the NYC Department of Health and Mental Hygiene. Danya and Emily gave some great perspectives on sources of government data, ethical uses of...

Topics: Podcast, changelog, open source, oss, software, development, developer, hackerchangelog, ai,...

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1.0

Mar 6, 2021
03/21

by
Merge Conflict

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Users and developers are overwhelmed with options to monetize their mobile applications. With so many strategies, how do you know what to pick and what will work for your apps? James recently when through the tough choice between creating a paid app, using in app purchases, or just shoving ads into his latest app. This week we discuss all the options that have worked and what haven't worked in the world of monetization.SponsorBitrise: Continuous Integration and Delivery for your mobile apps....

Topics: Podcast, Xamarin, iOS, Android, UWP, Microsoft, .NET, C#, F#, Machine Learning, AI, Apps, Apple,...

Topics: Radio Program, American Roman Catholics, Heads of government, American lawyers, Political science,...

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Jun 28, 2018
06/18

by
Giri Gopalan; Saeqa Dil Vrtilek; Luke Bornn

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In X-ray binary star systems consisting of a compact object that accretes material from an orbiting secondary star, there is no straightforward means to decide if the compact object is a black hole or a neutron star. To assist this process we develop a Bayesian statistical model which makes use of the fact that X-ray binary systems appear to cluster based on their compact object type when viewed from a 3-dimensional coordinate system derived from X-ray spectral data, where the first coordinate...

Topics: Statistics, High Energy Astrophysical Phenomena, Applications, Astrophysics, Machine Learning

Source: http://arxiv.org/abs/1507.03538

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Jun 29, 2018
06/18

by
Yoshiyuki Kabashima; Tomoyuki Obuchi; Makoto Uemura

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Cross-validation (CV) is a technique for evaluating the ability of statistical models/learning systems based on a given data set. Despite its wide applicability, the rather heavy computational cost can prevent its use as the system size grows. To resolve this difficulty in the case of Bayesian linear regression, we develop a formula for evaluating the leave-one-out CV error approximately without actually performing CV. The usefulness of the developed formula is tested by statistical mechanical...

Topics: Machine Learning, Learning, Computing Research Repository, Statistics

Source: http://arxiv.org/abs/1610.07733

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3.0

Jun 29, 2018
06/18

by
Eric Bax; Farshad Kooti

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If classifiers are selected from a hypothesis class to form an ensemble, bounds on average error rate over the selected classifiers include a component for selectivity, which grows as the fraction of hypothesis classifiers selected for the ensemble shrinks, and a component for variety, which grows with the size of the hypothesis class or in-sample data set. We show that the component for selectivity asymptotically dominates the component for variety, meaning that variety is essentially free.

Topics: Machine Learning, Learning, Computing Research Repository, Statistics

Source: http://arxiv.org/abs/1610.01234

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4.0

Jun 28, 2018
06/18

by
Arild Nøkland

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The back-propagation algorithm is widely used for learning in artificial neural networks. A challenge in machine learning is to create models that generalize to new data samples not seen in the training data. Recently, a common flaw in several machine learning algorithms was discovered: small perturbations added to the input data lead to consistent misclassification of data samples. Samples that easily mislead the model are called adversarial examples. Training a "maxout" network on...

Topics: Statistics, Learning, Machine Learning, Computing Research Repository

Source: http://arxiv.org/abs/1510.04189

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3.0

Jun 29, 2018
06/18

by
Gaël Varoquaux; Pradeep Reddy Raamana; Denis Engemann; Andrés Hoyos-Idrobo; Yannick Schwartz; Bertrand Thirion

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Decoding, ie prediction from brain images or signals, calls for empirical evaluation of its predictive power. Such evaluation is achieved via cross-validation, a method also used to tune decoders' hyper-parameters. This paper is a review on cross-validation procedures for decoding in neuroimaging. It includes a didactic overview of the relevant theoretical considerations. Practical aspects are highlighted with an extensive empirical study of the common decoders in within-and across-subject...

Topics: Machine Learning, Statistics

Source: http://arxiv.org/abs/1606.05201

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Jun 30, 2018
06/18

by
Raja Giryes; Guillermo Sapiro; Alex M. Bronstein

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In this work we study the properties of deep neural networks (DNN) with random weights. We formally prove that these networks perform a distance-preserving embedding of the data. Based on this we then draw conclusions on the size of the training data and the networks' structure. A longer version of this paper with more results and details can be found in (Giryes et al., 2015). In particular, we formally prove in the longer version that DNN with random Gaussian weights perform a...

Topics: Neural and Evolutionary Computing, Statistics, Mathematics, Computing Research Repository,...

Source: http://arxiv.org/abs/1412.5896

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Jun 29, 2018
06/18

by
Evan Racah; Christopher Beckham; Tegan Maharaj; Prabhat; Christopher Pal

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The detection and identification of extreme weather events in large scale climate simulations is an important problem for risk management, informing governmental policy decisions and advancing our basic understanding of the climate system. Recent work has shown that fully supervised convolutional neural networks (CNNs) can yield acceptable accuracy for classifying well-known types of extreme weather events when large amounts of labeled data are available. However, there are many different types...

Topics: Machine Learning, Computer Vision and Pattern Recognition, Computing Research Repository, Statistics

Source: http://arxiv.org/abs/1612.02095

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3.0

Jun 29, 2018
06/18

by
Yuval Harel; Ron Meir; Manfred Opper

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The process of dynamic state estimation (filtering) based on point process observations is in general intractable. Numerical sampling techniques are often practically useful, but lead to limited conceptual insight about optimal encoding/decoding strategies, which are of significant relevance to Computational Neuroscience. We develop an analytically tractable Bayesian approximation to optimal filtering based on point process observations, which allows us to introduce distributional assumptions...

Topics: Machine Learning, Quantitative Biology, Neurons and Cognition, Statistics

Source: http://arxiv.org/abs/1609.03519

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Jun 29, 2018
06/18

by
Ajinkya More

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A number of classification problems need to deal with data imbalance between classes. Often it is desired to have a high recall on the minority class while maintaining a high precision on the majority class. In this paper, we review a number of resampling techniques proposed in literature to handle unbalanced datasets and study their effect on classification performance.

Topics: Learning, Machine Learning, Applications, Computing Research Repository, Statistics

Source: http://arxiv.org/abs/1608.06048

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2.0

Jun 30, 2018
06/18

by
Amir Globerson; Tim Roughgarden; David Sontag; Cafer Yildirim

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Structured prediction tasks in machine learning involve the simultaneous prediction of multiple labels. This is typically done by maximizing a score function on the space of labels, which decomposes as a sum of pairwise elements, each depending on two specific labels. Intuitively, the more pairwise terms are used, the better the expected accuracy. However, there is currently no theoretical account of this intuition. This paper takes a significant step in this direction. We formulate the problem...

Topics: Machine Learning, Computing Research Repository, Data Structures and Algorithms, Learning,...

Source: http://arxiv.org/abs/1409.5834

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2.0

Jun 30, 2018
06/18

by
Atanu Kumar Ghosh; Arnab Chakraborty

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Conventional approaches of sampling signals follow the celebrated theorem of Nyquist and Shannon. Compressive sampling, introduced by Donoho, Romberg and Tao, is a new paradigm that goes against the conventional methods in data acquisition and provides a way of recovering signals using fewer samples than the traditional methods use. Here we suggest an alternative way of reconstructing the original signals in compressive sampling using EM algorithm. We first propose a naive approach which has...

Topics: Machine Learning, Computing Research Repository, Statistics, Learning, Methodology

Source: http://arxiv.org/abs/1405.5311

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5.0

Jun 30, 2018
06/18

by
Antoine Bonnefoy; Valentin Emiya; Liva Ralaivola; Rémi Gribonval

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Recent computational strategies based on screening tests have been proposed to accelerate algorithms addressing penalized sparse regression problems such as the Lasso. Such approaches build upon the idea that it is worth dedicating some small computational effort to locate inactive atoms and remove them from the dictionary in a preprocessing stage so that the regression algorithm working with a smaller dictionary will then converge faster to the solution of the initial problem. We believe that...

Topics: Machine Learning, Computing Research Repository, Statistics, Learning

Source: http://arxiv.org/abs/1412.4080

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2.0

Jun 30, 2018
06/18

by
Mathieu Lagrange; Grégoire Lafay; Boris Defreville; Jean-Julien Aucouturier

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The "bag-of-frames" approach (BOF), which encodes audio signals as the long-term statistical distribution of short-term spectral features, is commonly regarded as an effective and sufficient way to represent environmental sound recordings (soundscapes) since its introduction in an influential 2007 article. The present paper describes a concep-tual replication of this seminal article using several new soundscape datasets, with results strongly questioning the adequacy of the BOF...

Topics: Machine Learning, Computing Research Repository, Sound, Statistics

Source: http://arxiv.org/abs/1412.4052

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2.0

Jun 30, 2018
06/18

by
Scott W. Linderman; Ryan P. Adams

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Networks play a central role in modern data analysis, enabling us to reason about systems by studying the relationships between their parts. Most often in network analysis, the edges are given. However, in many systems it is difficult or impossible to measure the network directly. Examples of latent networks include economic interactions linking financial instruments and patterns of reciprocity in gang violence. In these cases, we are limited to noisy observations of events associated with each...

Topics: Machine Learning, Computing Research Repository, Statistics, Learning

Source: http://arxiv.org/abs/1402.0914

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3.0

Jun 30, 2018
06/18

by
Stephane Gaiffas; Bertrand Michel

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This paper is about variable selection, clustering and estimation in an unsupervised high-dimensional setting. Our approach is based on fitting constrained Gaussian mixture models, where we learn the number of clusters $K$ and the set of relevant variables $S$ using a generalized Bayesian posterior with a sparsity inducing prior. We prove a sparsity oracle inequality which shows that this procedure selects the optimal parameters $K$ and $S$. This procedure is implemented using a...

Topics: Machine Learning, Statistics

Source: http://arxiv.org/abs/1401.8017

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4.0

Jun 30, 2018
06/18

by
David Lopez-Paz; Suvrit Sra; Alex Smola; Zoubin Ghahramani; Bernhard Schölkopf

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Classical methods such as Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) are ubiquitous in statistics. However, these techniques are only able to reveal linear relationships in data. Although nonlinear variants of PCA and CCA have been proposed, these are computationally prohibitive in the large scale. In a separate strand of recent research, randomized methods have been proposed to construct features that help reveal nonlinear patterns in data. For basic tasks such...

Topics: Machine Learning, Computing Research Repository, Statistics, Learning

Source: http://arxiv.org/abs/1402.0119

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2.0

Jun 30, 2018
06/18

by
Duo Zhang; Benjamin I. P. Rubinstein; Jim Gemmell

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This paper explores combinatorial optimization for problems of max-weight graph matching on multi-partite graphs, which arise in integrating multiple data sources. Entity resolution-the data integration problem of performing noisy joins on structured data-typically proceeds by first hashing each record into zero or more blocks, scoring pairs of records that are co-blocked for similarity, and then matching pairs of sufficient similarity. In the most common case of matching two sources, it is...

Topics: Databases, Machine Learning, Computing Research Repository, Statistics, Learning

Source: http://arxiv.org/abs/1402.0282

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2.0

Jun 30, 2018
06/18

by
Aaron Karper

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The amount of information in the form of features and variables avail- able to machine learning algorithms is ever increasing. This can lead to classifiers that are prone to overfitting in high dimensions, high di- mensional models do not lend themselves to interpretable results, and the CPU and memory resources necessary to run on high-dimensional datasets severly limit the applications of the approaches. Variable and feature selection aim to remedy this by finding a subset of features that in...

Topics: Machine Learning, Computing Research Repository, Statistics, Learning, Artificial Intelligence

Source: http://arxiv.org/abs/1402.2300

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2.0

Jun 30, 2018
06/18

by
Nikhil Rao; Robert Nowak; Christopher Cox; Timothy Rogers

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Classification with a sparsity constraint on the solution plays a central role in many high dimensional machine learning applications. In some cases, the features can be grouped together so that entire subsets of features can be selected or not selected. In many applications, however, this can be too restrictive. In this paper, we are interested in a less restrictive form of structured sparse feature selection: we assume that while features can be grouped according to some notion of similarity,...

Topics: Machine Learning, Computing Research Repository, Statistics, Learning

Source: http://arxiv.org/abs/1402.4512

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2.0

Jun 30, 2018
06/18

by
Fang Han; Han Liu

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We propose a new high dimensional semiparametric principal component analysis (PCA) method, named Copula Component Analysis (COCA). The semiparametric model assumes that, after unspecified marginally monotone transformations, the distributions are multivariate Gaussian. COCA improves upon PCA and sparse PCA in three aspects: (i) It is robust to modeling assumptions; (ii) It is robust to outliers and data contamination; (iii) It is scale-invariant and yields more interpretable results. We prove...

Topics: Machine Learning, Statistics

Source: http://arxiv.org/abs/1402.4507

Topics: Radio Program, Metropolitan areas of China, Machine learning, Learning, Cybernetics, Port cities...

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2.0

Jun 30, 2018
06/18

by
Wei Liu

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Blind source separation (BSS) is one of the most important and established research topics in signal processing and many algorithms have been proposed based on different statistical properties of the source signals. For second-order statistics (SOS) based methods, canonical correlation analysis (CCA) has been proved to be an effective solution to the problem. In this work, the CCA approach is generalized to accommodate the case with added white noise and it is then applied to the BSS problem...

Topics: Mathematics, Numerical Analysis, Machine Learning, Statistics

Source: http://arxiv.org/abs/1403.2073

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2.0

Jun 30, 2018
06/18

by
Michael U. Gutmann; Ritabrata Dutta; Samuel Kaski; Jukka Corander

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Increasingly complex generative models are being used across disciplines as they allow for realistic characterization of data, but a common difficulty with them is the prohibitively large computational cost to evaluate the likelihood function and thus to perform likelihood-based statistical inference. A likelihood-free inference framework has emerged where the parameters are identified by finding values that yield simulated data resembling the observed data. While widely applicable, a major...

Topics: Computation, Machine Learning, Statistics, Methodology

Source: http://arxiv.org/abs/1407.4981

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2.0

Jun 30, 2018
06/18

by
Chicheng Zhang; Kamalika Chaudhuri

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We study agnostic active learning, where the goal is to learn a classifier in a pre-specified hypothesis class interactively with as few label queries as possible, while making no assumptions on the true function generating the labels. The main algorithms for this problem are {\em{disagreement-based active learning}}, which has a high label requirement, and {\em{margin-based active learning}}, which only applies to fairly restricted settings. A major challenge is to find an algorithm which...

Topics: Machine Learning, Computing Research Repository, Statistics, Learning

Source: http://arxiv.org/abs/1407.2657

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2.0

Jun 30, 2018
06/18

by
Truyen Tran; Dinh Phung; Svetha Venkatesh

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Learning structured outputs with general structures is computationally challenging, except for tree-structured models. Thus we propose an efficient boosting-based algorithm AdaBoost.MRF for this task. The idea is based on the realization that a graph is a superimposition of trees. Different from most existing work, our algorithm can handle partial labelling, and thus is particularly attractive in practice where reliable labels are often sparsely observed. In addition, our method works...

Topics: Machine Learning, Computing Research Repository, Statistics, Computer Vision and Pattern...

Source: http://arxiv.org/abs/1407.6432

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2.0

Jun 30, 2018
06/18

by
Truyen Tran; Dinh Phung; Svetha Venkatesh

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Ranking is a key aspect of many applications, such as information retrieval, question answering, ad placement and recommender systems. Learning to rank has the goal of estimating a ranking model automatically from training data. In practical settings, the task often reduces to estimating a rank functional of an object with respect to a query. In this paper, we investigate key issues in designing an effective learning to rank algorithm. These include data representation, the choice of rank...

Topics: Machine Learning, Computing Research Repository, Statistics, Information Retrieval, Learning

Source: http://arxiv.org/abs/1407.6089

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2.0

Jun 30, 2018
06/18

by
Ariel Jaffe; Boaz Nadler; Yuval Kluger

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In various situations one is given only the predictions of multiple classifiers over a large unlabeled test data. This scenario raises the following questions: Without any labeled data and without any a-priori knowledge about the reliability of these different classifiers, is it possible to consistently and computationally efficiently estimate their accuracies? Furthermore, also in a completely unsupervised manner, can one construct a more accurate unsupervised ensemble classifier? In this...

Topics: Machine Learning, Computing Research Repository, Statistics, Learning

Source: http://arxiv.org/abs/1407.7644

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3.0

Jun 29, 2018
06/18

by
Andee Kaplan; Daniel Nordman; Stephen Vardeman

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A restricted Boltzmann machine (RBM) is an undirected graphical model constructed for discrete or continuous random variables, with two layers, one hidden and one visible, and no conditional dependency within a layer. In recent years, RBMs have risen to prominence due to their connection to deep learning. By treating a hidden layer of one RBM as the visible layer in a second RBM, a deep architecture can be created. RBMs are thought to thereby have the ability to encode very complex and rich...

Topics: Machine Learning, Learning, Computing Research Repository, Statistics

Source: http://arxiv.org/abs/1612.01158

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5.0

Jun 30, 2018
06/18

by
Joon Hee Choi; S. V. N. Vishwanathan

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We present a technique for significantly speeding up Alternating Least Squares (ALS) and Gradient Descent (GD), two widely used algorithms for tensor factorization. By exploiting properties of the Khatri-Rao product, we show how to efficiently address a computationally challenging sub-step of both algorithms. Our algorithm, DFacTo, only requires two sparse matrix-vector products and is easy to parallelize. DFacTo is not only scalable but also on average 4 to 10 times faster than competing...

Topics: Machine Learning, Statistics

Source: http://arxiv.org/abs/1406.4519